CoRelatE: Learning the correlation in multi-fold relations for knowledge graph embedding

Huang, Yan; Sun, Haili; Xu, Ke; Lu, Songfeng; Wang, Tongyang; Zhang, Xinfang

doi:10.1016/j.knosys.2020.106601

Cited by 9 publications

(4 citation statements)

References 2 publications

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“…Graph embedding, or graph representation learning, is a machine learning approach, capable to convert nodes (log entries) in the heterogeneous graph into low-dimension vectors. Approaches like CoRelatE (Huang et al, 2021) study correlations between entities, facts, and relationships from instances in sequences in the form of natural language, and then build knowledge graphs.…”

Section: Graphical Modelsmentioning

confidence: 99%

“…Correlation methods based on ontologies and knowledge graphs cope well with the analysis of such features. However, as a rule, shallow and deep learning models use encoding of such functions, for example, one-hot encoding, which, in turn, reduces the efficiency of processing high cardinal values.The development of algorithms for learning ontologies and event databases are a good help in processing of such data (Huang et al, 2021). • Analysis of large and/or heterogeneous data.…”

Section: Summary Of Ai-based Security Event Correlation Modelsmentioning

confidence: 99%

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A survey on artificial intelligence techniques for security event correlation: models, challenges, and opportunities

Gaifulina

Kotenko

2023

Artif Intell Rev

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Information systems need to process a large amount of event monitoring data. The process of finding the relationships between events is called correlation, which creates a context between independent events and previously collected information in real time and normalizes it for subsequent processing. In cybersecurity, events can determine the steps of attackers and can be analyzed as part of a specific attack strategy. In this survey, we present the systematization of security event correlation models in terms of their representation in AI-based monitoring systems as: rule-based, semantic, graphical and machine learning based-models. We define the main directions of current research in the field of AI-based security event correlation and the methods used for the correlation of both single events and their sequences in attack scenarios. We also describe the prospects for the development of hybrid correlation models. In conclusion, we identify the existing problems in the field and possible ways to overcome them.

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Section: Graphical Modelsmentioning

confidence: 99%

Section: Summary Of Ai-based Security Event Correlation Modelsmentioning

confidence: 99%

A survey on artificial intelligence techniques for security event correlation: models, challenges, and opportunities

Gaifulina

Kotenko

2023

Artif Intell Rev

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“…Financial modelers are responsible for measuring the dependencies between market returns estimated with a multivariate model. Numerous papers have shown that the correlation between two variables can vary considerably from independence to nonlinear dependence, with complex forms that take all of the characteristics into account (e.g., Hoese and Huschens 2013;Diaz et al 2016;Huang et al 2021;Al-Awadhi et al 2020). Analysts have relied on the simplest dependency measure, the "Pearson correlation", which is capable of capturing the inherent dependency structure presented between the series and leading to the optimal portfolio.…”

Section: Literature Reviewmentioning

confidence: 99%

Interdependence between the BRICS Stock Markets and the Oil Price since the Onset of Financial and Economic Crises

Bouslama

2023

JRFM

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In this paper, we use a copula to examine the relationship and dynamic dependence structure between the crude oil market and the BRICS countries’ stock indices expressed through financial crises, from the 2008 global financial crisis to COVID-19, based on daily data. We characterize the long-term relationship as well as the short-term dynamics and represent the interdependence between them. We also study the short-run conditional links through the considered variables under the effects of long-run interactions and the asymmetric volatility spillover relationship. In addition, we establish that the volatility transmission is stubborn and that the impact of the crises and our empirical findings prove that there is fractional co-integration between crude oil and financial markets. We notice that there are lengthy correlations between the variables, as we detect significant bidirectional causal links. In particular, we see positive short-run links and use an optimal copula coefficient to measure the risk spillovers between oil markets and financial markets that represent the dependence structure. For robustness purposes, based on a sliding-window analysis, we complement our investigation with VaR analysis.

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“…The work orientation has been distributed in the variety of ramification of ways to model facts this is carefully associated with statistics graphs, a lot of which encode each commercial enterprise and relationships in a low-dimensional low vector continuous velocity (Huang et al, 2021). The relationship capabilities among the key companies and with a low-vector space determined as…”

Section: Work Orientationmentioning

confidence: 99%

Learning of Knowledge Graphs with Entity Descriptions

Conde¹

2022

IJMRA

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The representing learning makes specialty of knowledge graph and it indicates the difference between different entities. The knowledge graph representing with the low-dimensional space. In fact, most of the method usually concern of description of entity which is hard for existing strategies to take benefit of. Here, we recommend a new representing learning method with knowledge graphs that uses entity description. We evaluate our method on two tasks like knowledge graph and entity classification. Experimental effects on actual-world datasets show that our version plays higher than different baseline fashions, especially under the zero-short setting, which indicate that our technique for novel the entity description.

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CoRelatE: Learning the correlation in multi-fold relations for knowledge graph embedding

Cited by 9 publications

References 2 publications

A survey on artificial intelligence techniques for security event correlation: models, challenges, and opportunities

A survey on artificial intelligence techniques for security event correlation: models, challenges, and opportunities

Interdependence between the BRICS Stock Markets and the Oil Price since the Onset of Financial and Economic Crises

Learning of Knowledge Graphs with Entity Descriptions

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